Optimizing EDM of Gunmetal with Al2O3-Enhanced Dielectric - Experimental Insights and Machine Learning Models

Aug 4, 2026

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This study optimizes electric discharge machining (EDM) parameters for gunmetal using copper electrodes in two dielectric environments: conventional EDM oil and EDM oil infused with Al2O3 nanoparticles. A Taguchi L27 orthogonal array evaluates the impact of current, voltage, and pulse-on time on material removal rate (MRR), electrode wear rate (EWR), and surface roughness (Ra, Rq, Rz). ANOVA quantifies parameter influence, while multiple machine learning models (including linear, ridge, SVR, random forest, gradient boosting, and neural networks) predict machining outcomes. Neural networks achieve the highest predictive accuracy and the nanoparticle dielectric delivers ~15% higher MRR, ~20% lower EWR, and ~10% better surface finish, supporting data-driven productivity, tool life, and surface quality improvements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Abstract

Introduction

EDM process principles and parameter factors

Dielectric oils and motivations for alternative dielectrics

Nanoparticle-infused dielectrics and research gap

Article Optimizing EDM of Gunmetal with Al 2O3-Enhanced Dielectric: Experimental Insights and Machine Learning Models Saumya Kanwal 1, Usha Sharma 2,*, Saurabh Chauhan 3, Anuj Kumar Sharma 1, Jitendra Kumar Katiyar 4, *, Rabesh Kumar Singh 5, * and Shalini Mohanty 6, * Academic Editor: Zhuangjian Liu Received: 31 July 2025 Revised: 17 September 2025 Accepted: 28 September 2025 Published: 2 October 2025 Citation: Kanwal, S.; Sharma, U.; Chauhan, S.; Sharma, A.K.; Katiyar, J.K.; Singh, R.K.; Mohanty, S. Optimizing EDM of Gunmetal with Al2O3-Enhanced Dielectric: Experimental Insights and Machine Learning Models. Materials 2025, 18, 4578. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ma18194578 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Centre for Advanced Studies, Lucknow 226031, Uttar Pradesh, India; [saumyakanwal1999@gmail.com](saumyakanwal1999@gmail.com) (S.K.); [anujksharma@cas.res.in](anujksharma@cas.res.in) (A.K.S.) 2 Department of Information Technology, Babu Banarasi Das Institute of Technology and Management, Lucknow 226028, Uttar Pradesh, India 3 Applied Science and Humanities Department, Rajkiya Engineering College, Kannauj 209732, Uttar Pradesh, India; [saurabhchauhan09@gmail.com](saurabhchauhan09@gmail.com) 4 Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India 5 Mechanical Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur 273010, Uttar Pradesh, India 6 Faculty of Engineering and Sciences, University of Greenwich, Chatham Maritime ME4 4TB, UK * [Correspondence: usha19792023@gmail.com](Correspondence: usha19792023@gmail.com) (U.S.); [jitendra.katiyar@manipal.edu](jitendra.katiyar@manipal.edu) (J.K.K.); [rasm.singh@gmail.com](rasm.singh@gmail.com) (R.K.S.); [s.mohanty@greenwich.ac.uk](s.mohanty@greenwich.ac.uk) (S.M.); Tel.: +44-1634883186 (S.M.) Abstract This study investigates the optimization of electric discharge machining (EDM) parameters for gunmetal using copper electrodes in two different dielectric environments, which are conventional EDM oil and EDM oil infused with Al2O3 nanoparticles. A Taguchi L27 orthogonal array design was used to evaluate the effects of current, voltage, and pulse-on time on Material Removal Rate (MRR), Electrode Wear Rate (EWR), and surface roughness (Ra, Rq, and Rz) . Analysis of Variance (ANOVA) was used to statistically evaluate the influence of each parameter on machining performance. In addition, machine learning models including Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Neural Networks were implemented to predict performance outcomes. The originality of this research is not only rooted in the introduction of new models; rather, it is also found in the comparative analysis of various machine learning methodologies applied to the performance of electrical discharge machining (EDM) utilizing Al2O3-enhanced dielectrics. This investigation focuses specifically on gunmetal, a material that has not been extensively studied within this framework. The nanoparticle-enhanced dielectric demonstrated improved machining performance, achieving approximately 15% higher MRR, 20% lower EWR, and 10% improved surface finish compared to conventional EDM oil. Neural Networks consistently outperformed other models in predictive accuracy. Results indicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality. Keywords: EDM; gunmetal; Al 2O3 nanop

FAQs

What dielectrics and electrode setup were used to machine gunmetal in this study?

Copper electrodes were used in two dielectric environments: conventional EDM oil and EDM oil infused with Al2O3 nanoparticles.

Which EDM input parameters were varied and how was their effect analyzed?

Current, voltage, and pulse-on time were varied using a Taguchi L27 orthogonal array, and ANOVA was used to statistically assess their influence.

How did the Al2O3-enhanced dielectric affect machining performance and which model predicted best?

Compared with conventional EDM oil, the nanoparticle-enhanced dielectric improved MRR by about 15%, reduced EWR by about 20%, and improved surface finish by about 10%. Neural networks consistently achieved the highest predictive accuracy.

Aug 4, 2026

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